No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by inceptyon-labs · MCP Server · ★ 78
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gargantua safe to install? View the security audit →
Gargantua Gargantua is a native macOS cleaner for developers and power users. It scans common cache, build-artifact, duplicate-file, and app-remnant locations, classifies findings by risk, explains why each item is considered safe or risky, and only removes files through explicit user-controlled flows. The project is built as a Swift Package with four executables: : the SwiftUI macOS app : a local Model Context Protocol server for automation-driven scans and guarded cleanups : a launchd-driven background runner used by Scheduled Scans : an SMAppService/XPC helper for operations that require elevated trust <img src="docs/site/screenshots/readme-dashboard.png" wid
| Stars | 78 |
| Forks | 6 |
| Language | Swift |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 68.8383507233058/100 |
| Last Updated | 2026-10-03 |
| Created | 2026-04-23 |
| Platforms | mcp |
| Est. Tokens | ~22k |
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gargantua is Native macOS system cleaner with YAML-driven safety rules, local AI explainability via MLX, and an MCP server for agent-controlled cleanup workflows.. It is categorized as a MCP Server with 78 GitHub stars.
gargantua is primarily written in Swift. It covers topics such as agpl, cleaner, macos.
You can find installation instructions and usage details in the gargantua GitHub repository at github.com/inceptyon-labs/gargantua. The project has 78 stars and 6 forks, indicating an active community.
gargantua is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to gargantua on Agent Skills Hub include HomeClaw, toolhive-studio, SAM. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: